Diarization Driven by the ASR Based Segmentation

ABSTRACT

An approach is provided that receives an audio stream and utilizes a voice activation detection (VAD) process to create a digital audio stream of voices from at least two different speakers. An automatic speech recognition (ASR) process is applied to the digital stream with the ASR process resulting in the spoken words to which a speaker turn detection (STD) process is applied to identify a number of speaker segments with each speaker segment ending at a word boundary. A speaker clustering algorithm is then applied to the speaker segments to associate one of the speakers with each of the speaker segments.

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINTINVENTOR

A first “grace period disclosure” was published in March 2017. Thispublication was entitled “SPEAKER DIARIZATION: A PERSPECTIVE ONCHALLENGES AND OPPORTUNITIES FROM THEORY TO PRACTICE.” The joint authorsof this publication were Kenneth Church, Weizhong Zhu, Josef Vopicka,Jason Pelecanos, Dimitrios Dimitriadis, and Petr Fousek. Kenneth Church,Dimitrios Dimitriadis, and Petr Fousek are co-inventors of the inventiondescribed and claimed in the present patent application, and are namedas such in the present patent application U.S. filing documents.However, Weizhong Zhu, Josef Vopicka, and Jason Pelecanos, althoughco-authors of the above-referenced publication did not contributematerial to this publication that was related to the subject matter ofthe present patent application. Thus, any material in theabove-referenced publication that is related to the present patentapplication was written by the present co-inventors, Kenneth Church,Dimitrios Dimitriadis, and Petr Fousek, and no other person.

A second “grace period disclosure” was published in August 2017. Thispublication was entitled “Developing On-Line Speaker DiarizationSystem”. The joint authors of this publication were DimitriosDimitriadis and Petr Fousek who are co-inventors of the inventiondescribed and claimed in the present patent application, and are namedas such in the present patent application U.S. filing documents.

BACKGROUND

The goal of the diarization systems is to extract information about thespeakers found in an audio document, such as the number of speakers,their turns, the timing of their turns, etc. In more detail, thediarization systems first find the speaker turns, and then extract thecorresponding information about the homogeneous speech segmentsattributed to a single speaker. The state-of-the-art approach forfinding such turns is to use a sliding window of fixed length andinvestigate whether the speakers have changed inside that window. Thisbrute-force approach estimates two Gaussian models for the left andright sub-segments and then compares the corresponding statistics. Oncethese candidate speaker turns are found, a second module assigns thesesub-segments to speaker clusters. Most often, the brute-force systemcreates a big number of false positives (i.e. the system finds a speakerturn when there is no actual turn). In order to remove some of thesefalse positives, the current systems rely on the clusteringpost-processing scheme. However, less accurate turn detection actuallyhurts the clustering process as well. There are several problems withthis approach: First, the audio in this window may contain non-verbalacoustic cues, such as silence, noise, background speech, etc. Thesecues cause artifacts that may skew the estimated statistics, thus makethe turn detection process noisier. Further, these artifacts can alsodeteriorate the clustering performance, lowering the overall diarizationperformance. Second, the detected turns are often found in the middle ofa word, and consequently the ASR (automated speech recognition)performance will be lower (when diarization is combined with an ASRsystem). In more detail, there is no constraint where the speaker turnscan be found, so it is possible that they can be found in the middle ofa word, or even when there is a transition from silence to speech (andvice versa). The ad-hoc turns create discontinuities in the speech flowand thus, ASR performance becomes worse. Finally, there is no constraintin the length of the two sub-segments. In such cases, sub-optimalstatistics estimation is caused affecting all the sub-sequent processes,as well.

BRIEF SUMMARY

An approach is provided that receives an audio stream and utilizes avoice activation detection (VAD) process to create a digital audiostream of voices from at least two different speakers. An automaticspeech recognition (ASR) process is applied to the digital stream withthe ASR process resulting in the spoken words to which a speaker turndetection (STD) process is applied to identify a number of speakersegments with each speaker segment ending at a word boundary. A speakerclustering algorithm is then applied to the speaker segments toassociate one of the speakers with each of the speaker segments.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosure,as defined solely by the claims, will become apparent in thenon-limiting detailed description set forth below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 illustrates an information handling system, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein;

FIG. 3 is an exemplary diagram depicting diarization of a discussionbeing driven by ASR based segmentation;

FIG. 4 is an exemplary flowchart depicting steps taken by a speaker turndetection process;

FIG. 5 is an exemplary flowchart depicting steps taken by a process thatuses voice distinction to identify different speakers in a discussion;

FIG. 6 is an exemplary flowchart depicting steps taken by a process thatuses meta-information identified in the content of the discussion toidentify speaker changes;

FIG. 7 is an exemplary flowchart depicting steps taken to complete thespeaker turn detection process; and

FIG. 8 is an exemplary flowchart depicting steps taken to build atranscript indicating text spoken by the different people in adiscussion as well as ingesting the transcript into a question answering(QA) system.

DETAILED DESCRIPTION

FIGS. 1-8 depict an approach that can be executed on the informationhandling system shown in FIGS. 1-2. The approach described in FIGS. 3-8provides for voice activity detection (VAD) to feed data to an automaticspeech recognition system that feeds data to a speaker turn detectionfunction that inputs data to a speaker clustering function.

The proposed approach can address most of the issues mentioned above forthe following reasons. First, the VAD followed by ASR filters out mostof the non-speech segments, improving consequently the quality of thestatistics. The ASR output now corresponds to specific words estimatedby the decoder, so there are no non-verbal segments included in thepost-ASR pipeline. A basic assumption utilized by the approach is thateach speaker will utter a whole word before a speaker turn occurs. Inother words, the speaker turns occur at the word boundaries, asestimated by the ASR output, rather than essentially anywhere in theaudio stream as implemented in traditional systems.

The benefits of this approach are significant including enhancedperformance and complexity. The turn detection is now performed on theword boundaries as provided by the ASR hypothesis. The benefits fromthis approach are multiple: ASR cannot be affected by the diarizationprocess (since diarization now follows the ASR process). The minimumlength of the investigated speech segments is now at least a word'slength, thus making the statistics estimation more robust. Also, thenumber of potential changes is now an order of magnitude smaller thanthe brute-force approach. For example, in this approach a one secondaudio segment may contain five words which could represent at most fivepotential speaker turns. In contrast, the brute force approach used intraditional systems investigates the speaker turns after every so manymilliseconds (e.g., 50 milliseconds, etc.), so that a one second audiomay represent, instead, up to 20 potential speaker turns. Consequently,the approach described herein is more efficient than traditional systemsallowing the approach to perform efficiently and in near real-time.

Finally, the proposed system is intuitively closer to the humanperception where every word is assigned to a single speaker. Theproposed system was also compared against the brute-force approach interms of the speaker clustering errors and the diarization errors werefound to be 40% lower. This difference in performance is expected forall the reasons explained above. The diarization error is measured asthe ratio of frames correctly assigned to each speaker over the numberof speech frames found (assigned to any of the speakers). In moredetail, 40% better performance means that 40% more frames are assignedto the correct speaker (if the found speech remains the same—somethingthat is directly affected by the VAD and the ASR performance).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. The following detailed description willgenerally follow the summary of the disclosure, as set forth above,further explaining and expanding the definitions of the various aspectsand embodiments of the disclosure as necessary.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 in a computer network 102. QA system 100may include knowledge manager 104, which comprises one or moreprocessors and one or more memories, and potentially any other computingdevice elements generally known in the art including buses, storagedevices, communication interfaces, and the like. Computer network 102may include other computing devices in communication with each other andwith other devices or components via one or more wired and/or wirelessdata communication links, where each communication link may comprise oneor more of wires, routers, switches, transmitters, receivers, or thelike. QA system 100 and network 102 may enable question/answer (QA)generation functionality for one or more content users. Otherembodiments may include QA system 100 interacting with components,systems, sub-systems, and/or devices other than those depicted herein.

QA system 100 may receive inputs from various sources. For example, QAsystem 100 may receive input from the network 102, a corpus ofelectronic documents 107 or other data, semantic data 108, and otherpossible sources of input. In one embodiment, some or all of the inputsto QA system 100 route through the network 102 and stored in knowledgebase 106. The various computing devices on the network 102 may includeaccess points for content creators and content users. Some of thecomputing devices may include devices for a database storing the corpusof data. The network 102 may include local network connections andremote connections in various embodiments, such that QA system 100 mayoperate in environments of any size, including local and global, e.g.,the Internet. Additionally, QA system 100 serves as a front-end systemthat can make available a variety of knowledge extracted from orrepresented in documents, network-accessible sources and/or structureddata sources. In this manner, some processes populate the knowledgemanager with the knowledge manager also including input interfaces toreceive knowledge requests and respond accordingly.

In one embodiment, a content creator creates content in a document 107for use as part of a corpus of data with QA system 100. The document 107may include any file, text, article, or source of data for use in QAsystem 100. Content users may access QA system 100 via a networkconnection or an Internet connection to the network 102, and may inputquestions to QA system 100, which QA system 100 answers according to thecontent in the corpus of data. As further described below, when aprocess evaluates a given section of a document for semantic content,the process can use a variety of conventions to query it from knowledgemanager 104. One convention is to send a well-formed question.

Semantic data 108 is content based on the relation between signifiers,such as words, phrases, signs, and symbols, and what they stand for,their denotation, or connotation. In other words, semantic data 108 iscontent that interprets an expression, such as by using Natural LanguageProcessing (NLP). In one embodiment, the process sends well-formedquestions (e.g., natural language questions, etc.) to QA system 100 andQA system 100 may interpret the question and provide a response thatincludes one or more answers to the question. In some embodiments, QAsystem 100 may provide a response to users in a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 102. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems shown in FIG. 1 depicts separate nonvolatile data stores (server160 utilizes nonvolatile data store 165, and mainframe computer 170utilizes nonvolatile data store 175. The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 235 to Trusted Platform Module (TPM) 295.Other components often included in Southbridge 235 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 235to nonvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 0.802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 200 and another computer system or device.Optical storage device 290 connects to Southbridge 235 using Serial ATA(SATA) bus 288. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 235to other forms of storage devices, such as hard disk drives. Audiocircuitry 260, such as a sound card, connects to Southbridge 235 via bus258. Audio circuitry 260 also provides functionality such as audioline-in and optical digital audio in port 262, optical digital outputand headphone jack 264, internal speakers 266, and internal microphone268. Ethernet controller 270 connects to Southbridge 235 using a bus,such as the PCI or PCI Express bus. Ethernet controller 270 connectsinformation handling system 200 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIG. 3 is an exemplary diagram depicting diarization of a discussionbeing driven by ASR based segmentation. Discussion box 300 depicts twoor more people having a discussion, such as an interview, dialogue, orthe like. The discussion is recorded by microphone 305.

Voice Activity Detection (VAD) process 310 is a traditional VAD processthat detects the presence or absence of human speech. VAD is used inspeech coding and speech recognition. The sounds (human speech) detectedby process 310 are stored in digital sound stream 320.

Automatic Speech Recognition (ASR) process 330 processes audio stream320 and results in a sequence of words in the order that the words werespoken by the participants of the discussion. The spoken words thatresult from process 330 are stored in data store 340.

Speaker Turn Detection (STD) process 350 is applied to the spoken wordsand results in numerous speaker segments where each speaker segment endson a word boundary. The speaker segments with speaker changes that occuron word boundaries are stored in data store 360.

Speaker clustering algorithm 370 is then applied to the speakersegments. The speaker clustering algorithm associates one of the humanspeakers with each one of the speaker segments. The result of speakerclustering 370 is a transcript of the multiple speakers that indicatewhich speaker spoke which segments during the discussion. The transcriptof the multiple speakers is stored in data store 380.

In one embodiment, the transcript that is stored in data store 380 isingested to question answering (QA) system 100 using ingestion process390 where the transcript is ingested into corpus 106 that is utilized bythe QA system when answering questions. In one embodiment, processes310, 330, 350, 370, and 390 can be performed sequentially with thediscussion being ingested into QA system in near real time from the timethat the discussion occurred.

FIG. 4 is an exemplary flowchart depicting steps taken by a speaker turndetection process utilized in the approach described herein. FIG. 4processing commences at 400 and shows the steps taken by the SpeakerTurn Detection process. At step 410, the process selects the first soundsegment from the audio sound stream that is stored in data store 320.The process determines as to whether any spoken words are detected inthe selected sound segment (decision 420). If any spoken words aredetected in the selected sound segment, then decision 420 branches tothe ‘yes’ branch whereupon, at step 430 the process performs a speech totext conversion process on the sound segment generating one or morewords that are stored in data store 340. On the other hand, if no spokenwords are detected in the selected sound segment, then decision 420branches to the ‘no’ branch bypassing step 430. The process determinesas to whether there are any more sound segments in data store 320 toprocess (decision 440). If there are more sound segments, then decision440 branches to the ‘yes’ branch which loops back to step 410 to selectand process the next sound segment as described above. This loopingcontinues until all of the sound segments have been processed, at whichpoint decision 440 branches to the ‘no’ branch exiting the loop.

The process determines as to whether, as configured, the variousspeakers in the digital sound stream are being distinguished based ontheir voice characteristics (decision 450). If speakers are beingdistinguished based on their voice characteristics, then decision 450branches to the ‘yes’ branch whereupon, at predefined process 460, theprocess performs the Voice Distinction to Identify Different Speakersroutine (see FIG. 5 and corresponding text for processing details) withpredefined process 460 adding speaker change indicators and data to datastore 360. On the other hand, if speakers are not being distinguishedbased on their voice characteristics, then decision 450 branches to the‘no’ branch bypassing predefined process 460.

The process determines as to whether, as configured, the variousspeakers in the digital sound stream are being distinguished based ontheir meta-information derived from the content included in the varioussound segments (decision 470). If speakers are being distinguished basedon such meta-information, then decision 470 branches to the ‘yes’ branchwhereupon, at predefined process 480, the process performs theMeta-Information to Identify Different Speakers routine (see FIG. 6 andcorresponding text for processing details) with predefined process 480adding speaker change indicators and data to data store 360. On theother hand, if speakers are not being distinguished based on themeta-information derived from the content in the sound segments, thendecision 450 branches to the ‘no’ branch bypassing predefined process480.

At predefined process 490, the process performs the Complete SpeakerTurn Detection routine (see FIG. 7 and corresponding text for processingdetails), with predefined process 490 completing the speaker turndetection processing that commenced in FIG. 4. In one embodiment,predefined process 490 takes the sound segments and uses the data andspeaker change indicators stored in data store 360 to generate atranscript of the discussion between the multiple people with indicatorsshowing which speakers spoke which sound segments. This transcript isstored in data store 380 and can be further ingested into a questionanswering (QA) system, as shown as element 100 in FIG. 1. FIG. 4processing thereafter ends at 495.

FIG. 5 is an exemplary flowchart depicting steps taken by a process thatuses voice distinction to identify different speakers in a discussion.FIG. 5 processing commences at 500 and shows the steps taken by aprocess that uses voice distinction to identify the different speakersin a discussion. At step 510, the process selects the first word thatwas spoken with distinction of speakers being performed on wordboundaries, rather than sampling at a particular timing interval. Atstep 520, the process compares the vocal qualities, or (characteristics,found in the selected word to the voice qualities of the last identifiedspeaker (if at least one word has already been processed, otherwise thefirst word is associated with the first speaker). The process determineswhether the same speaker that spoke the selected word also spoke thepreviously spoken word (decision 525). If the words were spoken by thesame speaker, then decision 525 branches to the ‘yes’ branch whereupon,at step 530, the process associates the selected word with the lastidentified speaker, thus continuing the speaker's string of wordsspoken.

On the other hand, if the comparison reveals that the selected word wasspoken by someone other than the person that spoke the previous word,then decision 525 branches to the ‘no’ branch to perform steps 535through 580. At step 535, the process marks the word position (boundarybetween the previously spoken word and the selected word) as a placewhere a speaker change occurred and stores the data in data store 360.At step 540, the process compares the vocal qualities, orcharacteristics, found in the selected word to the vocal qualities ofthe other (previously) identified speakers with the other speakerquality data being retrieved from memory area 520. The process nextdetermines whether the speaker of the selected word has already beenidentified with a successful match of the vocal qualities of theselected word and the vocal qualities of one of the already knownspeakers (decision 550).

If the speaker matching the vocal qualities has already been identified,then decision 550 branches to the ‘yes’ branch to perform steps 560 and570. On the other hand, if step 540 was unable to match the vocalqualities of the selected word with an already known speaker(representing a new speaker to the discussion), then decision 550branches to the ‘no’ branch to perform steps 575 and 580. Steps 560 and570 are performed when the vocal qualities of the selected word match analready known speaker. At step 560, the process associates the selectedword with an already identified speaker and also sets the last speakervariable to the identified speaker (e.g., Speaker “B”, etc.). At step570, the process augments the training of the identified speaker withthe vocal qualities found in the selected word. In one embodiment, themore words that are trained as coming from a particular speaker, thebetter the process is at making a successful comparison at step 540.This augmented training data is stored in memory area 520 and associatedwith the identified speaker.

Steps 575 and 580 are performed when the vocal qualities of the selectedword did not match any of the already known speakers (representing a newspeaker to the discussion). At step 575, the process associates theselected word with a new speaker (e.g., speaker CB′, “C”, etc.) and setsthe last speaker variable to the new speaker. At step 580, the processinitializes training of the new speaker's voice characteristics (e.g.,speaker CB′, etc.) using the voice qualities found in the selected word.

The process next determines whether there are more words needed to beprocessed (decision 590). If there are more words to process, thendecision 590 branches to the ‘yes’ branch which loops back to step 510to select and process the next word from data store 340 as describedabove. This looping continues until all of the words have beenprocessed, at which point decision 590 branches to the ‘no’ branchexiting the loop. FIG. 5 processing thereafter returns to the callingroutine (see FIG. 4) at 595.

FIG. 6 is an exemplary flowchart depicting steps taken by a process thatuses meta-information identified in the content of the discussion toidentify speaker changes. FIG. 6 processing commences at 600 and showsthe steps taken by a process that uses meta-information extracted fromthe content of sound segments to identify the different speakers in adiscussion. At step 605, the process selects the first word from datastore 340. At step 610, the process initializes the speaker changevariable to low value with a low value indicating that a speaker changeis less likely, while a higher value indicates that a speaker change ismore likely. The process determines as to whether the selected word isat the end of a sentence or phrase (decision 615). If the word is at theend of a sentence or phrase, then decision 615 branches to the ‘yes’branch whereupon at step 620, the process keeps speaker change value lowas word is in middle of language segment. Note that conversational cues,such as interruptions, can be identified using the vocal qualitycharacteristics process shown in FIG. 5.

On the other hand, if the word is not at the end of a sentence orphrase, then decision 615 branches to the ‘no’ branch to perform steps625 through 675. At step 625, the process builds a last vocal segment(e.g., a phrase, sentence, etc.) that was ended by the selected word andstores the last segment in memory area 630. At step 635, the processanalyzes the last segment using a language model in light of previoussegments that were spoken. In one embodiment, the language model isdependent on the language (e.g., English, Spanish, etc.) that is beingspoken during the discussion. At step 650, the process increases thespeaker change value if the analysis reveals that the last segment isfound to be a question. At step 660, the process increases the speakerchange value if the analysis reveals that the last segment is found tobe a statement. At step 665, the process increases speaker change valueif the analysis reveals that the last segment is found to be a reply. Atstep 670, the process decreases the speaker change if the analysisreveals that the last segment is found to be a continuation of one ormore previous (successive) segments. At step 675, the process appendsthe latest (current) segment to the string of previous segments storedin memory area 640.

The process determines whether there are more words to process from datastore 340 (decision 680). If there are more words to process, thendecision 680 branches to the ‘yes’ branch which loops back to select andprocess the next word as described above. This looping continues untilall of the words have been processed, at which point decision 680branches to the ‘no’ branch exiting the loop. FIG. 6 processingthereafter returns to the calling routine (see FIG. 4) at 695.

FIG. 7 is an exemplary flowchart depicting steps taken to complete thespeaker turn detection process. FIG. 7 processing commences at 700 andshows the steps taken by a process that completes the speaker turndetection process that commenced in FIG. 4. At step 710, the processselects the first speaker change from data store 360. At step 720, theprocess identifies the position of the selected speaker change value inmemory area 620 with the speaker change values being provided bymeta-information derived from the content analysis shown in FIG. 6. Atstep 730, the process combines the meta-information change value(s)generated from the content analysis with the voice distinction dataprovided by the vocal analysis process shown in FIG. 5 with thecombination used to determine whether a speaker change occurred.

The process determines as to whether the combination of themeta-information speaker change value and the voice distinction speakerchange value indicates that a speaker change occurred at this location,or position, in the discussion (decision 740). If a speaker change isfound to occur at this location, then decision 740 branches to the ‘yes’branch whereupon, at step the speaker change is confirmed as occurringat this location. On the other hand, if no speaker change is found tooccur at this location, then decision 740 branches to the ‘no’ branchwhereupon, at step 760, the process removes the speaker change from thislocation in data store 360 as the process has found that a speakerchange actually did not occur at this location in the discussion.

The process determines whether there are more speaker changes in datastore 360 yet to be processed (decision 770). If there are more speakerchanges, then decision 770 branches to the ‘yes’ branch which loops backto step 710 to select and process the next set of speaker change data asdescribed above. This looping continues until there are no more speakerchanges to process, at which point decision 770 branches to the ‘no’branch exiting the loop. At step 775, the process identifies any speakerchange values that exceed a threshold and are not yet associated withspeaker change. These would be speaker changes indicated by themeta-information and stored in memory area 620 but not necessarilyindicated by the voice quality differentiation data detected and storedin data store 360. At step 780, the process re-analyzes the vocalquality and characteristics data at the locations where the speakerchange value from the meta-information indicated a possible speakerchange and the process updates any speaker changes as needed as resultof the re-analysis. The updates to the speaker changes are stored indata store 360.

At predefined process 790, the process performs the Build Transcriptroutine (see FIG. 8 and corresponding text for processing details).Predefined process 790 builds a transcript indicating the speaker ofparticular segments found in the audio stream with the textualtranscript stored in data store 380.

FIG. 8 is an exemplary flowchart depicting steps taken to build atranscript indicating text spoken by the different people in adiscussion as well as ingesting the transcript into a question answering(QA) system. FIG. 8 processing commences at 800 and shows the stepstaken by a process that builds a transcript detailing the discussionbetween two or more people and indicating which speaker said whichwords. At step 810, the process selects the first speaker change that isstored in data store 360. At step 820, the process retrieves the speakerthat was identified as saying the words (e.g., “speaker ‘A’, “John Doe,”etc.). At step 830, the process retrieves a string, or stream, of wordsup to the next speaker change or until the end of the file with thewords spoken being received from data store 340 and the next speakerchange being retrieved from data store 360. At step 840, the processwrites the identifier of the speaker and the words that were spoken bythis speaker to the transcript that is stored in data store 380. Theprocess determines whether there are more speaker changes to process indata store 360 (decision 850). If there are more speaker changes, thendecision 850 branches to the ‘yes’ branch which loops back to step 810to select and process the next speaker change as described above. Thislooping continues until there are no more speaker changes, at whichpoint decision 850 branches to the ‘no’ branch exiting the loop.

The process determines as to whether the transcript that was created andstored in data store 380 is to be ingested into the corpus utilized by aquestion answering (QA) system (decision 860). If the transcript isbeing ingested, then decision 860 branches to the ‘yes’ branchwhereupon, at step 870, the transcript stored in data store 380 isingested into corpus 106 that is utilized by QA system 100 system 100.On the other hand, the transcript is not being ingested, then decision860 branches to the ‘no’ branch bypassing step 870. FIG. 8 processingthereafter returns to the calling routine (see FIG. 7) at 895.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. Furthermore, it is to be understood that thedisclosure is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to disclosures containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an”;the same holds true for the use in the claims of definite articles.

1. A method implemented by an information handling system that includesa memory and a processor, the method comprising: receiving an audiostream that comprises both a plurality of speech segments correspondingto a plurality of human speakers and a plurality of non-verbal segments;utilizing a voice activation detection (VAD) process on the audiostream, wherein an output of the VAD process is a digital audio streamof voices corresponding to the plurality of speech segments; inputtingthe VAD process output into an automatic speech recognition (ASR)process, wherein an output of the ASR process comprises in a pluralityof spoken words corresponding to the plurality of speech segments and isdevoid of the plurality of non-verbal segments; inputting the ASRprocess output to a speaker turn detection (STD) process, wherein theSTD process generates a plurality of speaker segments that each end at aword boundary of one of the plurality of spoken words; and applying aspeaker clustering algorithm to the plurality of speaker segments,wherein the speaker clustering algorithm associates an identifier of oneof the human speakers with each of the speaker segments.
 2. The methodof claim 1 further comprising: generating a textual transcript of theaudio stream by outputting each of the speaker segments and theidentifier of the associated human speaker.
 3. The method of claim 1further comprising: ingesting the textual transcript into a questionanswering (QA) system corpus.
 4. The method of claim 1 furthercomprising: identifying a plurality of sets of vocal qualities from theaudio stream, wherein each of the sets of vocal qualities corresponds toa different one of the plurality of human speakers; comparing theplurality of sets of vocal qualities to each of the plurality of spokenwords; and associating one of the human speakers to each of the wordsbased on the comparison.
 5. The method of claim 4 wherein a change froma first of the plurality of human speaker to a second of the pluralityof human speakers is limited to word boundaries found in the pluralityof spoken words.
 6. The method of claim 1 wherein the speaker detectionprocess further comprises: associating a first word from the pluralityof spoken words to a first set of vocal qualities; identifying a secondword from the plurality of spoken words that is successive to the firstword and corresponds to a second set of vocal qualities; inserting aspeaker change mark between the first word and the second word inresponse to determining that the first set of vocal qualities isdifferent from the second set of vocal qualities; adjusting a speakerchange probability value in response to determining that the first wordis at an end of a question; and maintaining the speaker change markbetween the first word and the second word based on the adjusted speakerchange probability value.
 7. The method of claim 6 further comprising:analyzing a selected one of the speaker segments corresponding to thefirst word using a language model, wherein the analysis: increases thespeaker change probability value in response to the selected speakersegment indicating a statement; increases the speaker change probabilityvalue in response to the selected speaker segment indicating a reply;and decreases the speaker change probability value in response to theselected speaker segment indicating a continuation of a previous speakersegment; and identifying the second word based on the speaker changeprobability value and the comparison of the second word to the first setof vocal qualities.
 8. An information handling system comprising: one ormore processors; a memory coupled to at least one of the processors; anda set of computer program instructions stored in the memory and executedby at least one of the processors in order to perform actions of:receiving an audio stream that comprises both a plurality of speechsegments corresponding to a plurality of human speakers and a pluralityof non-verbal segments; utilizing a voice activation detection (VAD)process on the audio stream, wherein an output of the VAD process is adigital audio stream of voices corresponding to the plurality of speechsegments; inputting the VAD process output into an automatic speechrecognition (ASR) process, wherein an output of the ASR processcomprises in a plurality of spoken words corresponding to the pluralityof speech segments and is devoid of the plurality of non-verbalsegments; inputting the ASR process output to a speaker turn detection(STD) process, wherein the STD process generates a plurality of speakersegments that each end at a word boundary of one of the plurality ofspoken words; and applying a speaker clustering algorithm to theplurality of speaker segments, wherein the speaker clustering algorithmassociates an identifier of one of the human speakers with each of thespeaker segments.
 9. The information handling system of claim 8 whereinthe actions further comprise: generating a textual transcript of theaudio stream by outputting each of the speaker segments and theidentifier of the associated human speaker.
 10. The information handlingsystem of claim 8 wherein the actions further comprise: ingesting thetextual transcript into a question answering (QA) system corpus.
 11. Theinformation handling system of claim 8 wherein the actions furthercomprise: identifying a plurality of sets of vocal qualities from theaudio stream, wherein each of the sets of vocal qualities corresponds toa different one of the plurality of human speakers; comparing theplurality of sets of vocal qualities to each of the plurality of spokenwords; and associating one of the human speakers to each of the wordsbased on the comparison.
 12. The information handling system of claim 11wherein a change from a first of the plurality of human speaker to asecond of the plurality of human speakers is limited to word boundariesfound in the plurality of spoken words.
 13. The information handlingsystem of claim 8 wherein the actions further comprise: associating afirst word from the plurality of spoken words to a first set of vocalqualities; identifying a second word from the plurality of spoken wordsthat is successive to the first word and corresponds to a second set ofvocal qualities; inserting a speaker change mark between the first wordand the second word in response to determining that the first set ofvocal qualities is different from the second set of vocal qualities;adjusting a speaker change probability value in response to determiningthat the first word is at an end of a question; and maintaining thespeaker change mark between the first word and the second word based onthe adjusted speaker change probability value.
 14. The informationhandling system of claim 13 wherein the actions further comprise:analyzing a selected one of the speaker segments corresponding to thefirst word using a language model, wherein the analysis: increases thespeaker change probability value in response to the selected speakersegment indicating a statement; increases the speaker change probabilityvalue in response to the selected speaker segment indicating a reply;and decreases the speaker change probability value in response to theselected speaker segment indicating a continuation of a previous speakersegment; and identifying the second word based on the speaker changeprobability value and the comparison of the second word to the first setof vocal qualities.
 15. A computer program product stored in a computerreadable storage medium, comprising computer program code that, whenexecuted by an information handling system, causes the informationhandling system to perform actions comprising: receiving an audio streamthat comprises both a plurality of speech segments corresponding to aplurality of human speakers and a plurality of non-verbal segments;utilizing a voice activation detection (VAD) process on the audiostream, wherein an output of the VAD process is a digital audio streamof voices corresponding to the plurality of speech segments; inputtingthe VAD process output into an automatic speech recognition (ASR)process, wherein an output of the ASR process comprises in a pluralityof spoken words corresponding to the plurality of speech segments and isdevoid of the plurality of non-verbal segments; inputting the ASRprocess output to applying a speaker turn detection (STD) process,wherein the STD process generates a plurality of speaker segments thateach end at a word boundary of one of the plurality of spoken words; andapplying a speaker clustering algorithm to the plurality of speakersegments, wherein the speaker clustering algorithm associates anidentifier of one of the human speakers with each of the speakersegments.
 16. The computer program product of claim 15 wherein theactions further comprise: generating a textual transcript of the audiostream by outputting each of the speaker segments and the identifier ofthe associated human speaker; and ingesting the textual transcript intoa question answering (QA) system corpus.
 17. The computer programproduct of claim 15 wherein the actions further comprise: identifying aplurality of sets of vocal qualities from the audio stream, wherein eachof the sets of vocal qualities corresponds to a different one of theplurality of human speakers; comparing the plurality of sets of vocalqualities to each of the plurality of spoken words; and associating oneof the human speakers to each of the words based on the comparison. 18.The computer program product of claim 17 wherein a change from a firstof the plurality of human speaker to a second of the plurality of humanspeakers is limited to word boundaries found in the plurality of spokenwords.
 19. The computer program product of claim 15 wherein the actionsfurther comprise: associating a first word from the plurality of spokenwords to a first set of vocal qualities; identifying a second word fromthe plurality of spoken words that is successive to the first word andcorresponds to a second set of vocal qualities; inserting a speakerchange mark between the first word and the second word in response todetermining that the first set of vocal qualities is different from thesecond set of vocal qualities; adjusting a speaker change probabilityvalue in response to determining that the first word is at an end of aquestion; and maintaining the speaker change mark between the first wordand the second word based on the adjusted speaker change probabilityvalue.
 20. The computer program product of claim 19 wherein the actionsfurther comprise: analyzing a selected one of the speaker segmentscorresponding to the first word using a language model, wherein theanalysis: increases the speaker change probability value in response tothe selected speaker segment indicating a statement; increases thespeaker change probability value in response to the selected speakersegment indicating a reply; and decreases the speaker change probabilityvalue in response to the selected speaker segment indicating acontinuation of a previous speaker segment; and identifying the secondword based on the speaker change probability value and the comparison ofthe second word to the first set of vocal qualities.